Mandava Pitchaiah, Kent Thomas A
Department of Neurology, MSEE, MEDVAMC/Baylor College of Medicine, Houston, TX 770303, USA.
Stroke. 2009 May;40(5):1803-10. doi: 10.1161/STROKEAHA.108.532820. Epub 2009 Mar 12.
Many early phase trials in stroke have not been subsequently confirmed. Randomization balance in baseline factors that influence outcome are difficult to achieve and may be partly responsible for misleading early results. We hypothesized that comparison with an outcome function derived from a large number of pooled control arms would mitigate these randomization problems and provide a reliable predictor for decision-making before proceeding to later phase trials. We developed such a model and added a novel feature of generation of multidimensional 95% prediction surfaces by which individual studies could be compared. We performed a proof-of-principle study with published clinical trials, determining whether our method correctly identified known outcomes.
The control arms from all randomized, controlled trials for acute stroke with >or=10 subjects, including baseline National Institute of Health Stroke Scale, age, and 3-month outcomes published between 1994 and May 2008, were identified. A Matlab program (PPREDICTS) was written to generate outcome functions based on these parameters. Published treatment trials were compared with these 95% intervals to determine whether it successfully identified positive and negative trials.
Models of mortality and functional outcome were successfully generated (mortality: R(2)=0.69; functional outcome, modified Rankin Scale 0 to 2: R(2)=0.81; both P<0.0001). The National Institute of Neurological Diseases and Stroke intravenous recombinant tissue plasminogen activator trial and 3 studies yet to be subjected to Phase III study had modified Rankin Scale 0 to 2 outcomes above the 95% prediction interval. Sixteen treatment arm outcomes fell within prediction surface bounds. This group included 2 major trials, Stroke-Acute Ischemic NXY Treatment and Abciximab Emergent Stroke Treatment Trial, that initially appeared promising but went on to negative Phase III results.
This proof-of-principle analysis confirmed all positive and negative clinical stroke trial results and identified some promising therapies. The use of a pooled standard treatment group function combined with statistical bounds may improve selection of early studies for further study. This method may be applicable to any condition in which baseline factors influence outcome and at any stage of the development process.
许多中风的早期阶段试验结果随后并未得到证实。影响预后的基线因素的随机化平衡难以实现，这可能是导致早期结果产生误导的部分原因。我们假设，与从大量汇总的对照臂得出的预后函数进行比较，将减轻这些随机化问题，并在进入后期试验之前为决策提供可靠的预测指标。我们开发了这样一个模型，并添加了生成多维95%预测面的新特性，通过该特性可以对各个研究进行比较。我们用已发表的临床试验进行了一项原理验证研究，以确定我们的方法是否能正确识别已知的预后情况。
确定了所有随机对照的急性中风试验中受试者≥10例的对照臂，包括基线美国国立卫生研究院卒中量表、年龄以及1994年至2008年5月期间发表的3个月预后情况。编写了一个Matlab程序（PPREDICTS），根据这些参数生成预后函数。将已发表的治疗试验与这些95%区间进行比较，以确定其是否成功识别出阳性和阴性试验。
成功生成了死亡率和功能预后模型（死亡率：R² = 0.69；功能预后，改良Rankin量表0至2级：R² = 0.81；两者P < 0.0001）。美国国立神经疾病与中风研究所静脉注射重组组织型纤溶酶原激活剂试验以及3项尚未进行III期研究的试验，其改良Rankin量表0至2级的预后情况高于95%预测区间。16个治疗臂的预后情况落在预测面范围内。该组包括2项主要试验，即急性缺血性中风NXY治疗试验和阿昔单抗急性中风治疗试验，这两项试验最初看似很有前景，但后续的III期试验结果为阴性。
这项原理验证分析证实了所有阳性和阴性临床中风试验结果，并识别出了一些有前景的疗法。使用汇总的标准治疗组函数并结合统计界限，可能会改善早期研究的选择，以便进一步研究。该方法可能适用于任何基线因素影响预后的情况以及开发过程的任何阶段。
